datashader is a library that provides a flexible pipeline for doing bin-based rendering of large datasets. datashader is independent of Bokeh, but is designed to work well with it, providing raw images that Bokeh can then annotate, format, and display interactively.
- Provides automatic, nearly parameter-free visualization of datasets
- Allows extensive customization of each step in the data-processing pipeline
- Supports automatic downsampling and re-rendering with Bokeh and the Jupyter notebook
- Works well with dask and numba to handle very large datasets in and out of core (with examples using billions of datapoints)
The new features are described at https://github.com/bokeh/datashader/releases, including legends, hover support, colormaps, backgrounds, compositing, point sizing/shapes, line plotting, and new examples of Census data, time series data, and trajectories. And it’s now even faster.
Datashader can be installed using:
conda install datashader
and extensive examples are available as Jupyter notebooks at: